scholarly journals Satellite and In Situ Salinity: Understanding Near-Surface Stratification and Subfootprint Variability

2016 ◽  
Vol 97 (8) ◽  
pp. 1391-1407 ◽  
Author(s):  
J. Boutin ◽  
Y. Chao ◽  
W. E. Asher ◽  
T. Delcroix ◽  
R. Drucker ◽  
...  
Keyword(s):  
Hydrological Cycle ◽  
Ocean Dynamics ◽  
Sea Surface Salinity ◽  
Spatial Average ◽  
Surface Salinity ◽  
Near Surface ◽  
In Situ Data ◽  
Microwave Radiometers ◽  
Vertical Gradients

Abstract Remote sensing of salinity using satellite-mounted microwave radiometers provides new perspectives for studying ocean dynamics and the global hydrological cycle. Calibration and validation of these measurements is challenging because satellite and in situ methods measure salinity differently. Microwave radiometers measure the salinity in the top few centimeters of the ocean, whereas most in situ observations are reported below a depth of a few meters. Additionally, satellites measure salinity as a spatial average over an area of about 100 × 100 km2. In contrast, in situ sensors provide pointwise measurements at the location of the sensor. Thus, the presence of vertical gradients in, and horizontal variability of, sea surface salinity complicates comparison of satellite and in situ measurements. This paper synthesizes present knowledge of the magnitude and the processes that contribute to the formation and evolution of vertical and horizontal variability in near-surface salinity. Rainfall, freshwater plumes, and evaporation can generate vertical gradients of salinity, and in some cases these gradients can be large enough to affect validation of satellite measurements. Similarly, mesoscale to submesoscale processes can lead to horizontal variability that can also affect comparisons of satellite data to in situ data. Comparisons between satellite and in situ salinity measurements must take into account both vertical stratification and horizontal variability.

High-resolution sea surface salinity and temperature in coastal areas from Sentinel-2 and Copernicus Marine in situ data

2020 ◽  
Author(s):  
Encarni Medina-Lopez
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
In Situ Data ◽  
The Neural Network ◽  
Sentinel 2

<p>The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m x 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82% and 84% for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 C and 0.4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.</p>

scholarly journals Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in Arctic and Sub-Arctic Regions

2018 ◽  
Vol 10 (11) ◽  
pp. 1772 ◽  
Author(s):  
Estrella Olmedo ◽  
Carolina Gabarró ◽  
Verónica González-Gambau ◽  
Justino Martínez ◽  
Joaquim Ballabrera-Poy ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Remotely Sensed ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Surface Salinity ◽  
In Situ Data ◽  
Arctic Regions ◽  
The Difference

This paper aims to present and assess the quality of seven years (2011–2017) of 25 km nine-day Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) objectively analyzed maps in the Arctic and sub-Arctic oceans ( 50 ∘ N– 90 ∘ N). The SMOS SSS maps presented in this work are an improved version of the preliminary three-year dataset generated and freely distributed by the Barcelona Expert Center. In this new version, a time-dependent bias correction has been applied to mitigate the seasonal bias that affected the previous SSS maps. An extensive database of in situ data (Argo floats and thermosalinograph measurements) has been used for assessing the accuracy of this product. The standard deviation of the difference between the new SMOS SSS maps and Argo SSS ranges from 0.25 and 0.35. The major features of the inter-annual SSS variations observed by the thermosalinographs are also captured by the SMOS SSS maps. However, the validation in some regions of the Arctic Ocean has not been feasible because of the lack of in situ data. In those regions, qualitative comparisons with SSS provided by models and the remotely sensed SSS provided by Aquarius and SMAP have been performed. Despite the differences between SMOS and SMAP, both datasets show consistent SSS variations with respect to the model and the river discharge in situ data, but present a larger dynamic range than that of the model. This result suggests that, in those regions, the use of the remotely sensed SSS may help to improve the models.

Pi-MEP Salinity – an ESA-NASA Platform for sustained satellite surface salinity validation 

2021 ◽  
Author(s):  
Roberto Sabia ◽  
Sebastien Guimbard ◽  
Nicolas Reul ◽  
Tony Lee ◽  
Julian Schanze ◽  
...  
Keyword(s):  
Program Planning ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
In Situ Data ◽  
Sea Level Anomalies ◽  
Ocean Salinity ◽  
Salinity Data ◽  
Planning Group

<p>The Pilot Mission Exploitation Platform (Pi-MEP) for Salinity (www.salinity-pimep.org) has been released operationally in 2019 to the broad oceanographic community, in order to foster satellite sea surface salinity validation and exploitation activities.</p><p>Specifically, the Platform aims at enhancing salinityvalidation, by allowing systematic inter-comparison of various EO datasets with a broad suite of in-situ data, and also at enabling oceanographic process studies by capitalizing on salinity data in synergy with additional spaceborne estimates.</p><p> </p><p>Despite Pi-MEP was originally conceived as an ESA initiative to widen the uptake of the Soil Moisture and Ocean Salinity (SMOS) mission data over ocean, a project partnership with NASA was devised soon after the operational deployment, and an official collaboration endorsed within the ESA-NASA Joint Program Planning Group (JPPG).</p><p> </p><p>The Salinity Pi-MEP has therefore become a reference hub for SMOS, SMAP and Aquarius satellite salinity missions, which are assessed in synergy with additional thematic datasets (e.g., precipitation, evaporation, currents, sea level anomalies, ocean color, sea surface temperature). </p><p>Match-up databases of satellite/in situ (such as Argo, TSG, moorings, drifters) data and corresponding validation reports at different spatiotemporal scales are systematically generated; furthermore, recently-developed dedicated tools allow data visualization, metrics computation and user-driven features extractions.</p><p> </p><p>The Platform is also meant to monitor salinity in selected oceanographic “case studies”, ranging from river plumes monitoring to SSS characterization in challenging regions, such as high latitudes or semi-enclosed basins.</p><p> </p><p>The two Agencies are currently collaborating to widen the Platform features on several technical aspects - ranging from a triple-collocation software implementation to a sustained exploitation of data from the SPURS-1/2 campaigns. In this context, an upgrade of the satellite/in-situ match-up methodology has been recently agreed, resulting into a redefinition of the validation criteria that will be subsequently implemented in the Platform.</p><p> </p><p>A further synthesis of the three satellites salinity algorithms, models and auxiliary data handling is at the core of the ESA Climate Change Initiative (CCI) on Salinity and of ESA-NASA further collaboration.</p>

scholarly journals Spatial Scales of Sea Surface Salinity Subfootprint Variability in the SPURS Regions

2020 ◽  
Vol 12 (23) ◽  
pp. 3996
Author(s):  
Frederick M. Bingham ◽  
Zhijin Li
Keyword(s):  
North Atlantic ◽  
Spatial Scales ◽  
Remote Sensing Data ◽  
Ocean Modeling ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Regional Ocean Modeling System ◽  
Surface Salinity ◽  
In Situ Data

Subfootprint variability (SFV), or representativeness error, is variability within the footprint of a satellite that can impact validation by comparison of in situ and remote sensing data. This study seeks to determine the size of the sea surface salinity (SSS) SFV as a function of footprint size in two regions that were heavily sampled with in situ data. The Salinity Processes in the Upper-ocean Regional Studies-1 (SPURS-1) experiment was conducted in the subtropical North Atlantic in the period 2012–2013, whereas the SPURS-2 study was conducted in the tropical eastern North Pacific in the period 2016–2017. SSS SFV was also computed using a high-resolution regional model based on the Regional Ocean Modeling System (ROMS). We computed SFV at footprint sizes ranging from 20 to 100 km for both regions. SFV is strongly seasonal, but for different reasons in the two regions. In the SPURS-1 region, the meso- and submesoscale variability seemed to control the size of the SFV. In the SPURS-2 region, the SFV is much larger than SPURS-1 and controlled by patchy rainfall.

scholarly journals Near-Surface Salinity Reveals the Oceanic Sources of Moisture for Australian Precipitation through Atmospheric Moisture Transport

2020 ◽  
Vol 33 (15) ◽  
pp. 6707-6730
Author(s):  
Saurabh Rathore ◽  
Nathaniel L. Bindoff ◽  
Caroline C. Ummenhofer ◽  
Helen E. Phillips ◽  
Ming Feng
Keyword(s):  
El Niño ◽  
Moisture Transport ◽  
Hydrological Cycle ◽  
El Nino ◽  
Southern Oscillation ◽  
Sea Surface Salinity ◽  
Atmospheric Moisture ◽  
Surface Salinity ◽  
Near Surface ◽  
Atmospheric Moisture Transport

AbstractThe long-term trend of sea surface salinity (SSS) reveals an intensification of the global hydrological cycle due to human-induced climate change. This study demonstrates that SSS variability can also be used as a measure of terrestrial precipitation on interseasonal to interannual time scales, and to locate the source of moisture. Seasonal composites during El Niño–Southern Oscillation/Indian Ocean dipole (ENSO/IOD) events are used to understand the variations of moisture transport and precipitation over Australia, and their association with SSS variability. As ENSO/IOD events evolve, patterns of positive or negative SSS anomaly emerge in the Indo-Pacific warm pool region and are accompanied by atmospheric moisture transport anomalies toward Australia. During co-occurring La Niña and negative IOD events, salty anomalies around the Maritime Continent (north of Australia) indicate freshwater export and are associated with a significant moisture transport that converges over Australia to create anomalous wet conditions. In contrast, during co-occurring El Niño and positive IOD events, a moisture transport divergence anomaly over Australia results in anomalous dry conditions. The relationship between SSS and atmospheric moisture transport also holds for pure ENSO/IOD events but varies in magnitude and spatial pattern. The significant pattern correlation between the moisture flux divergence and SSS anomaly during the ENSO/IOD events highlights the associated ocean–atmosphere coupling. A case study of the extreme hydroclimatic events of Australia (e.g., the 2010/11 Brisbane flood) demonstrates that the changes in SSS occur before the peak of ENSO/IOD events. This raises the prospect that tracking of SSS variability could aid the prediction of Australian rainfall.

scholarly journals Subfootprint Variability of Sea Surface Salinity Observed during the SPURS-1 and SPURS-2 Field Campaigns

2019 ◽  
Vol 11 (22) ◽  
pp. 2689 ◽  
Author(s):  
Frederick M. Bingham
Keyword(s):  
Absolute Error ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Satellite Measurement ◽  
Surface Salinity ◽  
Passive Measurement ◽  
In Situ Data ◽  
Field Campaigns ◽  
Comparison Of The Results

Subfootprint variability (SFV), variability within the footprint of a satellite measurement, is a source of error associated with the validation process, especially for a satellite measurement with a large footprint such as those measuring sea surface salinity (SSS). This type of error has not been adequately quantified in the past. In this study, I have examined SFV using in situ ocean data from the SPURS-1 (Salinity Processes in the Upper ocean Regional Studies-1) and SPURS-2 field campaigns in the subtropical North Atlantic and eastern tropical North Pacific respectively. I computed SFV from these data over two one-year periods of intense sampling. The results show that SFV is highly seasonal. I have computed SFV errors in several different forms, a median value of the weekly snapshot error, a total snapshot error, an absolute error of the Aquarius and SMAP (Soil Moisture Active Passive) measurement, a part of that error associated with SFV and a bias due to the skewness of the distribution of SSS. These results are characteristic only of the particular regions studied. However, comparison of the results with high resolution models, and in situ data from moorings gives the possibility of getting global estimates of SFV from these other more common sources of SSS data.

scholarly journals Sea Surface Salinity Seasonal Variability in the Tropics from Satellites, Gridded In Situ Products and Mooring Observations

2020 ◽  
Vol 13 (1) ◽  
pp. 110
Author(s):  
Frederick M. Bingham ◽  
Susannah Brodnitz ◽  
Lisan Yu
Keyword(s):  
Seasonal Variability ◽  
Ground Truth ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Zero Bias ◽  
In Situ Data ◽  
The Tropics ◽  
Good Agreement

Satellite observations of sea surface salinity (SSS) have been validated in a number of instances using different forms of in situ data, including Argo floats, moorings and gridded in situ products. Since one of the most energetic time scales of variability of SSS is seasonal, it is important to know if satellites and gridded in situ products are observing the seasonal variability correctly. In this study we validate the seasonal SSS from satellite and gridded in situ products using observations from moorings in the global tropical moored buoy array. We utilize six different satellite products, and two different gridded in situ products. For each product we have computed seasonal harmonics, including amplitude, phase and fraction of variance (R2). These quantities are mapped for each product and for the moorings. We also do comparisons of amplitude, phase and R2 between moorings and all the satellite and gridded in situ products. Taking the mooring observations as ground truth, we find general good agreement between them and the satellite and gridded in situ products, with near zero bias in phase and amplitude and small root mean square differences. Tables are presented with these quantities for each product quantifying the degree of agreement.

scholarly journals New insights into SMOS Sea Surface Salinity retrievals in the Arctic Ocean.

2020 ◽  
Author(s):  
Alexandre Supply ◽  
Jacqueline Boutin ◽  
Jean-Luc Vergely ◽  
Nicolas Kolodziejczyk ◽  
Gilles Reverdin ◽  
...  
Keyword(s):  
Dielectric Constant ◽  
Arctic Ocean ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Surface Salinity ◽  
Near Surface ◽  
The Arctic Ocean ◽  
L Band

<p>Since 2010, the Soil Moisture and Ocean Salinity (SMOS) satellite mission monitors the earth emission at L-Band, providing the longest time series of Sea Surface Salinity (SSS) from space over the global ocean. However, retrieving SSS at high latitudes with a reasonable accuracy remains challenging, in particular due to the low sensitivity of L-Band radiometric measurements to SSS in cold waters and to the contamination of SMOS measurements by the vicinity of continents and sea ice as well as the presence of Radio Frequency Interferences. In this paper, we assess the quality of weekly SSS fields derived from swath-ordered instantaneous SMOS SSS (so called Level 2) distributed by the European Space Agency. These products are filtered according to new criteria. We use the pseudo-dielectric constant retrieved from SMOS brightness temperatures to filter SSS pixels polluted by sea ice. We identify that the dielectric constant model and the sea surface temperature auxiliary parameter used as prior information in the SMOS SSS retrieval are significant sources of uncertainty. We develop a novel correction methodology accordingly.</p><p>SSS Standard deviation of differences (STDD) between weekly SMOS SSS and in-situ near surface salinity significantly decrease after applying the SSS correction, from 1.46 pss to 1.26 pss. The correlation between new SMOS SSS and in-situ near surface salinity reaches 0.94. SMOS estimates better capture SSS variability in the Arctic Ocean in comparison to TOPAZ reanalysis (STDD = 1.86 pss), particularly in river plumes fresher by about 10 pss than surrounding waters. Furthermore, comparisons with in-situ measurements ranging from 1 to 11 m depths identify huge vertical stratification in fresh regions. This emphasizes the need to consider in-situ salinity as close as possible to the sea surface when validating L-band radiometric SSS which are representative of the first top centimeter.</p>

scholarly journals Vertical Variability of Near-Surface Salinity in the Tropics: Consequences for L-Band Radiometer Calibration and Validation

2010 ◽  
Vol 27 (1) ◽  
pp. 192-209 ◽  
Author(s):  
Claire Henocq ◽  
Jacqueline Boutin ◽  
Gilles Reverdin ◽  
François Petitcolin ◽  
Sabine Arnault ◽  
...  
Keyword(s):  
European Space Agency ◽  
Skin Depth ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Near Surface ◽  
Calibration And Validation ◽  
Space Agency ◽  
L Band ◽  
Rain Events

Abstract Two satellite missions are planned to be launched in the next two years; the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) and the National Aeronautics and Space Administration (NASA) Aquarius missions aim at detecting sea surface salinity (SSS) using L-band radiometry (1.4 GHz). At that frequency, the skin depth is on the order of 1 cm. However, the calibration and validation of L-band-retrieved SSS will be done with in situ measurements, mainly taken at 5-m depth. To anticipate and understand vertical salinity differences in the first 10 m of the ocean surface layer, in situ vertical profiles are analyzed. The influence of rain events is studied. Tropical Atmosphere Ocean (TAO) moorings, the most comprehensive dataset, provide measurements of salinity taken simultaneously at 1, 5, and 10 m and measurements of rain rate. Then, observations of vertical salinity differences, sorted according to their vertical levels, are expanded through the tropical band (30°S–30°N) using thermosalinographs (TSG), floats, expendable conductivity–temperature–depth (XCTD), and CTD data. Vertical salinity differences higher than 0.1 pss are observed in the Pacific, Atlantic, and Indian Oceans, mainly between 0° and 15°N, which coincides with the average position of the intertropical convergence zone (ITCZ). Some differences exceed 0.5 pss locally and persist for more than 10 days. A statistical approach is developed for the detection of large vertical salinity differences, knowing the history of rain events and the simultaneous wind intensity, as estimated from satellite measurements.

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